ACM -- Attribute Conditioning for Abstractive Multi Document Summarization

• URL: http://arxiv.org/abs/2205.03978v1
• Date: Mon, 9 May 2022 00:00:14 GMT
• Title: ACM -- Attribute Conditioning for Abstractive Multi Document Summarization
• Authors: Aiswarya Sankar, Ankit Chadha
• Abstract summary: We propose a model that incorporates attribute conditioning modules in order to decouple conflicting information by conditioning for a certain attribute in the output summary. This approach shows strong gains in ROUGE score over baseline multi document summarization approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Abstractive multi document summarization has evolved as a task through the basic sequence to sequence approaches to transformer and graph based techniques. Each of these approaches has primarily focused on the issues of multi document information synthesis and attention based approaches to extract salient information. A challenge that arises with multi document summarization which is not prevalent in single document summarization is the need to effectively summarize multiple documents that might have conflicting polarity, sentiment or subjective information about a given topic. In this paper we propose ACM, attribute conditioned multi document summarization,a model that incorporates attribute conditioning modules in order to decouple conflicting information by conditioning for a certain attribute in the output summary. This approach shows strong gains in ROUGE score over baseline multi document summarization approaches and shows gains in fluency, informativeness and reduction in repetitiveness as shown through a human annotation analysis study.

Related papers

• Unified Multi-Modal Interleaved Document Representation for Information Retrieval [57.65409208879344]
  We produce more comprehensive and nuanced document representations by holistically embedding documents interleaved with different modalities. Specifically, we achieve this by leveraging the capability of recent vision-language models that enable the processing and integration of text, images, and tables into a unified format and representation.
  arXiv  Detail & Related papers  (2024-10-03T17:49:09Z)
• Absformer: Transformer-based Model for Unsupervised Multi-Document Abstractive Summarization [1.066048003460524]
  Multi-document summarization (MDS) refers to the task of summarizing the text in multiple documents into a concise summary. Abstractive MDS aims to generate a coherent and fluent summary for multiple documents using natural language generation techniques. We propose Absformer, a new Transformer-based method for unsupervised abstractive summary generation.
  arXiv  Detail & Related papers  (2023-06-07T21:18:23Z)
• Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering [49.85790367128085]
  We pre-training a generic multi-document model from a novel cross-document question answering pre-training objective. This novel multi-document QA formulation directs the model to better recover cross-text informational relations. Unlike prior multi-document models that focus on either classification or summarization tasks, our pre-training objective formulation enables the model to perform tasks that involve both short text generation and long text generation.
  arXiv  Detail & Related papers  (2023-05-24T17:48:40Z)
• Mining both Commonality and Specificity from Multiple Documents for Multi-Document Summarization [1.4629756274247374]
  The multi-document summarization task requires the designed summarizer to generate a short text that covers the important information of original documents. This paper proposes a multi-document summarization approach based on hierarchical clustering of documents.
  arXiv  Detail & Related papers  (2023-03-05T14:25:05Z)
• Unsupervised Summarization with Customized Granularities [76.26899748972423]
  We propose the first unsupervised multi-granularity summarization framework, GranuSum. By inputting different numbers of events, GranuSum is capable of producing multi-granular summaries in an unsupervised manner.
  arXiv  Detail & Related papers  (2022-01-29T05:56:35Z)
• Modeling Endorsement for Multi-Document Abstractive Summarization [10.166639983949887]
  A crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s) In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization. Our method generates a synopsis from each document, which serves as an endorser to identify salient content from other documents.
  arXiv  Detail & Related papers  (2021-10-15T03:55:42Z)
• WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization [16.048329028104643]
  In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents. One major challenge for this task is the lack of availability of labeled training datasets. We propose a novel weakly supervised learning approach via utilizing distant supervision.
  arXiv  Detail & Related papers  (2020-11-03T02:02:55Z)
• SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy [92.5683788430012]
  SupMMD is a novel technique for generic and update summarization based on the maximum discrepancy from kernel two-sample testing. We show the efficacy of SupMMD in both generic and update summarization tasks by meeting or exceeding the current state-of-the-art on the DUC-2004 and TAC-2009 datasets.
  arXiv  Detail & Related papers  (2020-10-06T09:26:55Z)
• SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression [61.97200991151141]
  SummPip is an unsupervised method for multi-document summarization. We convert the original documents to a sentence graph, taking both linguistic and deep representation into account. We then apply spectral clustering to obtain multiple clusters of sentences, and finally compress each cluster to generate the final summary.
  arXiv  Detail & Related papers  (2020-07-17T13:01:15Z)
• Leveraging Graph to Improve Abstractive Multi-Document Summarization [50.62418656177642]
  We develop a neural abstractive multi-document summarization (MDS) model which can leverage well-known graph representations of documents. Our model utilizes graphs to encode documents in order to capture cross-document relations, which is crucial to summarizing long documents. Our model can also take advantage of graphs to guide the summary generation process, which is beneficial for generating coherent and concise summaries.
  arXiv  Detail & Related papers  (2020-05-20T13:39:47Z)
• A Divide-and-Conquer Approach to the Summarization of Long Documents [4.863209463405628]
  We present a novel divide-and-conquer method for the neural summarization of long documents. Our method exploits the discourse structure of the document and uses sentence similarity to split the problem into smaller summarization problems. We demonstrate that this approach paired with different summarization models, including sequence-to-sequence RNNs and Transformers, can lead to improved summarization performance.
  arXiv  Detail & Related papers  (2020-04-13T20:38:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.